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3D-USE: From Image-Level to Scene-Level Underwater Enhancement

arXiv:2608.28020v1 Announce Type: new Abstract: Underwater 3D reconstruction faithfully reproduces the color shifts and visibility loss of captured views, while physical inversion may leave estimation

local-aiarxiv-cs-cv

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A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation

DGX agent

arXiv:2608.28247v1 Announce Type: new Abstract: Change detection in Earth observation (EO) is critical for monitoring land surface transformations, yet recent research in the field is constrained by i

model-releasesarxiv-cs-cv

31 Aug 2026

A Controlled Audit of Architectural Complexity in Uncertainty-Aware Multi-Organ Ultrasound Classification

arXiv:2608.28063v1 Announce Type: new Abstract: Multi-organ ultrasound classifiers increasingly combine attention, mixture-of-experts routing, uncertainty gating, and evidential deep learning (EDL) ob

safetyarxiv-cs-cv

A Deeper Analysis of Block-Sparse Featurizers

arXiv:2608.27515v1 Announce Type: cross Abstract: The recently introduced block-sparse featurizer (BSF; Fel et al., 2026) is similar to a sparse autoencoder (SAE), but its atomic unit is a small subsp

A Formal Limitation on Learning Human Language From Textual Corpora

arXiv:2608.28560v1 Announce Type: new Abstract: Can a listener recover what a speaker means from the form of an utterance alone? We answer this question information-theoretically, and for a listener g

A Method for Layer Bit-Width Allocation in LLM Quantization via Performance Maximization Under a Quality-Degradation Constraint

arXiv:2608.28003v1 Announce Type: new Abstract: This paper proposes a layer bit allocation method for Gemma-3-1B, formulating the problem as performance maximization (latency decrease) given a degrada

A Multimodal Dataset of Student Oral Presentations with Sensors and Evaluation Data

arXiv:2601.07576v2 Announce Type: replace-cross Abstract: Oral presentation skills are a critical component of higher education, yet comprehensive datasets capturing real-world student performance acr

A-PAIR: A Benchmark and Identity-Consistent Grounding Framework for Air-Ground Cross-View Referring Person Detection

arXiv:2608.27997v1 Announce Type: new Abstract: Air-ground cross-view referring person detection is a necessary component in the language-to-perception-to-control chain of collective embodied intellig

A Probabilistic Interpretation of KV Cache Eviction

arXiv:2608.28293v1 Announce Type: new Abstract: The premise and promise of KV (cache) eviction is simple: higher throughput can be achieved by evicting some entries from the KV cache, at a negligible

A Shaky Voice Is Not Always a Dodge: Benchmarking Textual and Vocal Evasion Detection in Earnings Calls

arXiv:2608.28040v1 Announce Type: new Abstract: Existing approaches to evasion detection in earnings calls focus on textual transcripts, treating evasion as a single-dimensional phenomenon. We argue t

A Survey on Rubric-Guided Reinforcement Learning for Language Models

arXiv:2608.27505v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) has become the dominant paradigm for aligning large language models (LLMs) with human preferences. How

A Unified Framework to Elicit Structured Feedback for Interpretable Multi-Trait Essay Scoring

arXiv:2608.28407v1 Announce Type: new Abstract: Multi-trait Automated Essay Scoring (AES) requires rubric-grounded reasoning across interdependent traits, rather than isolated score prediction. Existi

Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures

arXiv:2608.27533v1 Announce Type: cross Abstract: We introduce a new ab initio approach to simulate semiconductor devices that integrates scalable machine-learned (ML) electronic structure models with

ABCD: Alpha-Composited Block Coordinate Descent: Constant-VRAM Training for Large Radiance Fields

arXiv:2608.27735v1 Announce Type: new Abstract: We present ABCD (Alpha-Composited Block Coordinate Descent), an out-of-core training framework for alpha-composited radiance fields, instantiated here f

local-aiarxiv-cs-cv

Abstract4D: A Large-Scale Dataset and Framework for Understanding the Visual Language of Abstract Art

arXiv:2608.28339v1 Announce Type: new Abstract: Artificial intelligence can classify artistic styles and synthesize images, but it still lacks a model of the visual language that gives art meaning. Ab

Accelerating LLM Inference via Vector Index Based Output Embeddings

arXiv:2608.27460v1 Announce Type: new Abstract: Large output embedding matrices create a significant memory bandwidth bottleneck during autoregressive decoding, especially for compact LLMs with large

Acquire, Repair, Preserve: A Diagnosis-Guided Post-Training Recipe for Small-Model Dialogue Game Agents

arXiv:2608.28458v1 Announce Type: new Abstract: Interactive dialogue games test a capability that static benchmarks largely leave implicit: a model must carry state across turns, interpret feedback, a

local-aiarxiv-cs-cl

AcrossVAM1.0: Particle World Modeling for Text-Assisted Robot Video Prediction

model-releasesarxiv-cs-ro

arXiv:2608.28491v1 Announce Type: cross Abstract: Predicting robot videos requires both precise motion reasoning and preservation of high-frequency appearance, yet monolithic pixel models entangle the

Applications

Actionable CBFI: Integrating Structural Decomposition and Causal Counterfactual Recourse for Tabular Machine Learning

arXiv:2608.27821v1 Announce Type: new Abstract: Explainable artificial intelligence (XAI) increasingly calls for actionable counterfactual recourse, yet current methodologies face challenges related t

applicationsarxiv-cs-lg

Activation Boundary Matching: Task-Informed Initialization for Low-Rank Adaptation

arXiv:2511.19145v4 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) is highly sensitive to initialization, yet existing schemes construct the initial subspace from statistics at the pretrai

Ada-TokenCom: Rate-Adaptive Token Communications via Large-Model-Driven Token Compression and Generation

arXiv:2608.28086v1 Announce Type: cross Abstract: Token Communications (TokenCom) has recently emerged as a new paradigm in which tokens serve as unified units for communication and computation, enabl

Advancing Interaction-Sensitive Feature Selection: Novel Relief-Based Algorithms, Expanded Comparisons, and Recommendations for Biomedical Data Mining

arXiv:2608.28552v1 Announce Type: new Abstract: As a precursor to high-dimensional biomedical data modeling, reliable feature selection can reduce computational expense, improve modeling performance,

Aero Hand Open: A Simulation-Ready Tendon-Driven Hand for Dexterous Manipulation Learning

arXiv:2608.28578v1 Announce Type: cross Abstract: Tendon-driven hands are anthropomorphic, and moving the actuators off the joints is what makes a hand of this capability affordable to build. Two effe

AI Alignment through a Game-theoretic Lens: A Survey

arXiv:2608.27910v1 Announce Type: cross Abstract: As large language models and increasingly capable AI agents are deployed in high-risk settings, aligning them with complex human values has become a c

AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not

arXiv:2608.27855v1 Announce Type: new Abstract: Text generated by large language models (LLMs) has been shown to be stylometrically distinct from human-written text itep{andreDetectingAIAuthorship2023

AIM: Anchor Identity Features, Then Match for Multimodal Large Language Model Unlearning

arXiv:2608.28312v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) can memorize identity-specific facts about people in their fine-tuning data, creating privacy risks when a pe

Aligning Agentic World Models via Knowledgeable Experience Learning

arXiv:2601.13247v2 Announce Type: replace Abstract: Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural grounding to

Amortizing intractable inference in diffusion models for vision, language, and control

arXiv:2405.20971v3 Announce Type: replace-cross Abstract: Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors in

Ampere: Communication-Efficient and High-Accuracy Split Federated Learning

arXiv:2507.07130v2 Announce Type: replace-cross Abstract: A Federated Learning (FL) system collaboratively trains neural networks across devices and a server but is limited by significant on-device co

local-aiarxiv-cs-lg

An algebraic proof of Colombo's difference-power determinant conjecture

arXiv:2608.28274v1 Announce Type: new Abstract: Let nge2 be even, let lambda=(lambda_1,ldots,lambda_n)inR^n have pairwise distinct coordinates, and define the difference-power matrix [ A_d(lambda) :=

An Enclosed Mode Is a Gauge Choice: Topology Relative to Reach in Certified Code World Models

arXiv:2608.28541v1 Announce Type: new Abstract: A code world model accepted by a sampling gate can be exactly right on everything the gate can see and arbitrarily wrong beyond it. We characterize what

An End-to-End Hybrid Quantum--Classical Sampling Workflow for Discrete Markov Random Fields: A Reproducible Case Study

arXiv:2607.09893v2 Announce Type: replace-cross Abstract: Sampling from discrete Markov random fields (MRFs) is a hard problem. We study amplitude-encoded i.i.d. sampling for small MRFs where 2^n targ

Anatomy-Aware Promptable Segmentation with Online Interactive Training for AUTOPET V

arXiv:2608.28461v1 Announce Type: new Abstract: We present an anatomy-aware, promptable model for whole-body lesion segmentation in FDG and PSMA PET/CT, developed for the AUTOPET V challenge. The prop

Anchored Scenario Coverage for Failure-Aware First-Hit Batch Inverse Design

arXiv:2608.27873v1 Announce Type: cross Abstract: Early discovery of at least one valid design satisfying a target requirement is a central objective in failure-prone closed-loop inverse design. A nat

ARC-CT: Anatomy-Routed Contrastive Vision-Language Learning for 3D Chest CT

arXiv:2608.28455v1 Announce Type: new Abstract: Contrastive vision-language learning uses paired chest CT volumes and radiology reports to learn abnormality classifiers without manually annotated labe

local-aiarxiv-cs-cv

Are These Modules Worth Their Cost? A Paradigm-Level Accuracy-Cost Analysis of In-context Learning Text-to-SQL

arXiv:2608.28432v1 Announce Type: new Abstract: Recent advances in in-context learning (ICL) text-to-SQL have substantially improved execution accuracy on public benchmarks by assembling increasingly

Aspiration-based Perturbed Learning Automata in Games with Noisy Utility Measurements. Part A: Stochastic Stability in Non-zero-Sum Games

arXiv:2511.11602v3 Announce Type: replace Abstract: Reinforcement-based learning has attracted considerable attention both in modeling human behavior as well as in engineering, for designing measureme

Attribute Token Arithmetic: Disentangled and Continuous Semantic Control for Visual Autoregressive Models

arXiv:2608.28082v1 Announce Type: new Abstract: Autoregressive text-to-image generation has recently achieved remarkable progress, offering high-fidelity synthesis via a unified generative framework.

Auditing Generative Audio Calls for Known-Task Audio-LLM Evaluation

arXiv:2608.27817v1 Announce Type: cross Abstract: Speech and audio LLMs are often evaluated by asking whether a waveform prompt beats an automatic speech recognition (ASR) transcript. For known closed

Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning

arXiv:2509.00094v2 Announce Type: replace-cross Abstract: Assessing spoken language is challenging, and quantifying pronunciation metrics for machine learning models is even harder. However, for the H

Applications

BanglaMed-QA: A Question Answering System for Healthcare Support in Bangla

arXiv:2608.28329v1 Announce Type: new Abstract: Medical question answering (QA) systems have become crucial tools for providing reliable health information. But they remain very unexplored for low-res

applicationsarxiv-cs-cl

Bayesian Experimental Design for Model Discrepancy Calibration: A Rivalry between Kullback--Leibler Divergence and Wasserstein Distance

arXiv:2601.16425v2 Announce Type: replace Abstract: Designing experiments that systematically gather data from complex physical systems is central to accelerating scientific discovery. While Bayesian

BEACON: Behavior-Anchored Cross-Source Knowledge Graph Construction for Cyber Threat Intelligence

arXiv:2608.28394v1 Announce Type: cross Abstract: Cyber threat intelligence (CTI) is foundational to modern cyber defense, yet much of it resides in unstructured reports whose volume and heterogeneity

Below the Noise Floor: Bimodal Seed Collapse and Distinct Failure Modes in Small-Model Knowledge Distillation

arXiv:2608.27729v1 Announce Type: new Abstract: Function routing -- selecting the correct API call from a fixed catalog given a natural-language request -- is a deployment problem where small students

Benchmarking General Mobile Assistants in Challenging Real-World Scenarios

arXiv:2608.27477v1 Announce Type: cross Abstract: Graphical user interfaces have emerged as an important environment for evaluating autonomous AI agents on multimodal interactive tasks. Existing bench

Hardware

Benchmarking large language model agent societies against human behavioural distributions

arXiv:2608.28182v1 Announce Type: cross Abstract: Populations of large language model agents are increasingly used as experimental societies. Three doubts shadow every such result: whether the agents

hardwarearxiv-cs-cl

Hardware

Beyond Data Scaling: Representation-Centric Continued Pre-training for Vision-Language-Action Models

arXiv:2608.27550v1 Announce Type: cross Abstract: Scaling robot data is crucial for building generalist Vision-Language-Action (VLA) models, yet robot trajectories are harder to scale than web-scale i

hardwarearxiv-cs-cv

Beyond Flat Netlist: Hierarchical Graph Representation Learning for Scalable Analysis of Sequential Circuits

arXiv:2608.28188v1 Announce Type: new Abstract: Circuit Representation Learning (CRL) offers a powerful paradigm to guide and optimize core Electronic Design Automation (EDA) tasks, but its practical

local-aiarxiv-cs-lg